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Add the max, min, maximum, minimum api for the API 2.0
* Add the max, min, maximum, minimum api for the API 2.0, test=develop
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
|
||
from __future__ import print_function | ||
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||
import unittest | ||
import numpy as np | ||
from op_test import OpTest, skip_check_grad_ci | ||
import paddle | ||
import paddle.fluid.core as core | ||
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class ApiMaxTest(unittest.TestCase): | ||
def setUp(self): | ||
if core.is_compiled_with_cuda(): | ||
self.place = core.CUDAPlace(0) | ||
else: | ||
self.place = core.CPUPlace() | ||
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def test_api(self): | ||
paddle.enable_static() | ||
with paddle.static.program_guard(paddle.static.Program(), | ||
paddle.static.Program()): | ||
data = paddle.nn.data("data", shape=[10, 10], dtype="float32") | ||
result_max = paddle.max(x=data, axis=1) | ||
exe = paddle.static.Executor(self.place) | ||
input_data = np.random.rand(10, 10).astype(np.float32) | ||
res, = exe.run(feed={"data": input_data}, fetch_list=[result_max]) | ||
self.assertEqual((res == np.max(input_data, axis=1)).all(), True) | ||
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with paddle.static.program_guard(paddle.static.Program(), | ||
paddle.static.Program()): | ||
data = paddle.nn.data("data", shape=[10, 10], dtype="int64") | ||
result_max = paddle.max(x=data, axis=0) | ||
exe = paddle.static.Executor(self.place) | ||
input_data = np.random.randint(10, size=(10, 10)).astype(np.int64) | ||
res, = exe.run(feed={"data": input_data}, fetch_list=[result_max]) | ||
self.assertEqual((res == np.max(input_data, axis=0)).all(), True) | ||
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def test_errors(self): | ||
paddle.enable_static() | ||
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def test_input_type(): | ||
with paddle.static.program_guard(paddle.static.Program(), | ||
paddle.static.Program()): | ||
data = np.random.rand(10, 10) | ||
result_max = paddle.max(x=data, axis=0) | ||
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self.assertRaises(TypeError, test_input_type) | ||
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def test_imperative_api(self): | ||
paddle.disable_static() | ||
np_x = np.array([10, 10]).astype('float64') | ||
x = paddle.to_variable(np_x) | ||
z = paddle.max(x, axis=0) | ||
np_z = z.numpy() | ||
z_expected = np.array(np.max(np_x, axis=0)) | ||
self.assertEqual((np_z == z_expected).all(), True) |
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
|
||
from __future__ import print_function | ||
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||
import unittest | ||
import numpy as np | ||
from op_test import OpTest, skip_check_grad_ci | ||
import paddle | ||
import paddle.fluid.core as core | ||
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class ApiMaximumTest(unittest.TestCase): | ||
def setUp(self): | ||
if core.is_compiled_with_cuda(): | ||
self.place = core.CUDAPlace(0) | ||
else: | ||
self.place = core.CPUPlace() | ||
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self.input_x = np.random.rand(10, 15).astype("float32") | ||
self.input_y = np.random.rand(10, 15).astype("float32") | ||
self.input_z = np.random.rand(15).astype("float32") | ||
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def test_static_api(self): | ||
paddle.enable_static() | ||
with paddle.static.program_guard(paddle.static.Program(), | ||
paddle.static.Program()): | ||
data_x = paddle.nn.data("x", shape=[10, 15], dtype="float32") | ||
data_y = paddle.nn.data("y", shape=[10, 15], dtype="float32") | ||
result_max = paddle.maximum(data_x, data_y) | ||
exe = paddle.static.Executor(self.place) | ||
res, = exe.run(feed={"x": self.input_x, | ||
"y": self.input_y}, | ||
fetch_list=[result_max]) | ||
self.assertEqual((res == np.maximum(self.input_x, self.input_y)).all(), | ||
True) | ||
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with paddle.static.program_guard(paddle.static.Program(), | ||
paddle.static.Program()): | ||
data_x = paddle.nn.data("x", shape=[10, 15], dtype="float32") | ||
data_z = paddle.nn.data("z", shape=[15], dtype="float32") | ||
result_max = paddle.maximum(data_x, data_z, axis=1) | ||
exe = paddle.static.Executor(self.place) | ||
res, = exe.run(feed={"x": self.input_x, | ||
"z": self.input_z}, | ||
fetch_list=[result_max]) | ||
self.assertEqual((res == np.maximum(self.input_x, self.input_z)).all(), | ||
True) | ||
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def test_dynamic_api(self): | ||
paddle.disable_static() | ||
np_x = np.array([10, 10]).astype('float64') | ||
x = paddle.to_variable(self.input_x) | ||
y = paddle.to_variable(self.input_y) | ||
z = paddle.maximum(x, y) | ||
np_z = z.numpy() | ||
z_expected = np.array(np.maximum(self.input_x, self.input_y)) | ||
self.assertEqual((np_z == z_expected).all(), True) | ||
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def test_broadcast_axis(self): | ||
paddle.disable_static() | ||
np_x = np.random.rand(5, 4, 3, 2).astype("float64") | ||
np_y = np.random.rand(4, 3).astype("float64") | ||
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x = paddle.to_variable(self.input_x) | ||
y = paddle.to_variable(self.input_y) | ||
result_1 = paddle.maximum(x, y, axis=1) | ||
result_2 = paddle.maximum(x, y, axis=-2) | ||
self.assertEqual((result_1.numpy() == result_2.numpy()).all(), True) |
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
|
||
from __future__ import print_function | ||
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import unittest | ||
import numpy as np | ||
from op_test import OpTest, skip_check_grad_ci | ||
import paddle | ||
import paddle.fluid.core as core | ||
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class ApiMinTest(unittest.TestCase): | ||
def setUp(self): | ||
if core.is_compiled_with_cuda(): | ||
self.place = core.CUDAPlace(0) | ||
else: | ||
self.place = core.CPUPlace() | ||
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def test_api(self): | ||
paddle.enable_static() | ||
with paddle.static.program_guard(paddle.static.Program(), | ||
paddle.static.Program()): | ||
data = paddle.nn.data("data", shape=[10, 10], dtype="float32") | ||
result_min = paddle.min(x=data, axis=1) | ||
exe = paddle.static.Executor(self.place) | ||
input_data = np.random.rand(10, 10).astype(np.float32) | ||
res, = exe.run(feed={"data": input_data}, fetch_list=[result_min]) | ||
self.assertEqual((res == np.min(input_data, axis=1)).all(), True) | ||
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with paddle.static.program_guard(paddle.static.Program(), | ||
paddle.static.Program()): | ||
data = paddle.nn.data("data", shape=[10, 10], dtype="int64") | ||
result_min = paddle.min(x=data, axis=0) | ||
exe = paddle.static.Executor(self.place) | ||
input_data = np.random.randint(10, size=(10, 10)).astype(np.int64) | ||
res, = exe.run(feed={"data": input_data}, fetch_list=[result_min]) | ||
self.assertEqual((res == np.min(input_data, axis=0)).all(), True) | ||
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def test_errors(self): | ||
paddle.enable_static() | ||
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def test_input_type(): | ||
with paddle.static.program_guard(paddle.static.Program(), | ||
paddle.static.Program()): | ||
data = np.random.rand(10, 10) | ||
result_min = paddle.min(x=data, axis=0) | ||
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self.assertRaises(TypeError, test_input_type) | ||
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def test_imperative_api(self): | ||
paddle.disable_static() | ||
np_x = np.array([10, 10]).astype('float64') | ||
x = paddle.to_variable(np_x) | ||
z = paddle.min(x, axis=0) | ||
np_z = z.numpy() | ||
z_expected = np.array(np.min(np_x, axis=0)) | ||
self.assertEqual((np_z == z_expected).all(), True) |
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@@ -0,0 +1,80 @@ | ||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
|
||
from __future__ import print_function | ||
|
||
import unittest | ||
import numpy as np | ||
from op_test import OpTest, skip_check_grad_ci | ||
import paddle | ||
import paddle.fluid.core as core | ||
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||
|
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class ApiMinimumTest(unittest.TestCase): | ||
def setUp(self): | ||
if core.is_compiled_with_cuda(): | ||
self.place = core.CUDAPlace(0) | ||
else: | ||
self.place = core.CPUPlace() | ||
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self.input_x = np.random.rand(10, 15).astype("float32") | ||
self.input_y = np.random.rand(10, 15).astype("float32") | ||
self.input_z = np.random.rand(15).astype("float32") | ||
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def test_static_api(self): | ||
paddle.enable_static() | ||
with paddle.static.program_guard(paddle.static.Program(), | ||
paddle.static.Program()): | ||
data_x = paddle.nn.data("x", shape=[10, 15], dtype="float32") | ||
data_y = paddle.nn.data("y", shape=[10, 15], dtype="float32") | ||
result_min = paddle.minimum(data_x, data_y) | ||
exe = paddle.static.Executor(self.place) | ||
res, = exe.run(feed={"x": self.input_x, | ||
"y": self.input_y}, | ||
fetch_list=[result_min]) | ||
self.assertEqual((res == np.minimum(self.input_x, self.input_y)).all(), | ||
True) | ||
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with paddle.static.program_guard(paddle.static.Program(), | ||
paddle.static.Program()): | ||
data_x = paddle.nn.data("x", shape=[10, 15], dtype="float32") | ||
data_z = paddle.nn.data("z", shape=[15], dtype="float32") | ||
result_min = paddle.minimum(data_x, data_z, axis=1) | ||
exe = paddle.static.Executor(self.place) | ||
res, = exe.run(feed={"x": self.input_x, | ||
"z": self.input_z}, | ||
fetch_list=[result_min]) | ||
self.assertEqual((res == np.minimum(self.input_x, self.input_z)).all(), | ||
True) | ||
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def test_dynamic_api(self): | ||
paddle.disable_static() | ||
np_x = np.array([10, 10]).astype('float64') | ||
x = paddle.to_variable(self.input_x) | ||
y = paddle.to_variable(self.input_y) | ||
z = paddle.minimum(x, y) | ||
np_z = z.numpy() | ||
z_expected = np.array(np.minimum(self.input_x, self.input_y)) | ||
self.assertEqual((np_z == z_expected).all(), True) | ||
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def test_broadcast_axis(self): | ||
paddle.disable_static() | ||
np_x = np.random.rand(5, 4, 3, 2).astype("float64") | ||
np_y = np.random.rand(4, 3).astype("float64") | ||
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x = paddle.to_variable(self.input_x) | ||
y = paddle.to_variable(self.input_y) | ||
result_1 = paddle.minimum(x, y, axis=1) | ||
result_2 = paddle.minimum(x, y, axis=-2) | ||
self.assertEqual((result_1.numpy() == result_2.numpy()).all(), True) |
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